Current (self-reported) fuel type
The numbers of observations with each current fuel type:
##
## Smokeles Smoky Wood_and_or_Plant
## 17 87 8
Primary analysis
Investigate the association with current (self-reported) fuel type in the LEX study participants, adjusting for known confounders and stove ventilation. The reference group for this analysis would be the smoky coal users. This would be a categorical analysis, and the results would be a p-value from the likelihood ratio (LR) test of a confounder-only model to a model including the exposure variables, as well as p-values for the contrast of each category of coal use (smokeless coal or plant/wood) to that of smoky coal. FDR correction should be used separately for each of these sets. The main interest would be in the coal-specific findings and perhaps less so in the results from the LR test.
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon
\end{aligned}
\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.2368 0.6340
## Hannum EAA 0.6304 0.6340
## PhenoAge EAA 0.5142 0.6340
## Skin&Blood EAA 0.4887 0.6340
## GrimAge EAA 0.0279 0.2232
## DNAmTL 0.5250 0.6340
## IEAA 0.3694 0.6340
## EEAA 0.6340 0.6340
Linear regression
In the following section, we performed linear regression with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon
\end{aligned}
\] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.
The estimations of \(\beta_0\), \(\beta_1\) and \(\beta_2\) with given \(Y\) are shown below. The \(\beta_1\) and \(\beta_2\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.
Sensitivity analyses
Likelihood ratio (LR) test (single model)
Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.
Full model: \[Y = \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.2800 0.8200
## Hannum EAA 0.4890 0.8819
## PhenoAge EAA 0.8936 0.8936
## Skin&Blood EAA 0.5512 0.8819
## GrimAge EAA 0.1672 0.8200
## DNAmTL 0.8624 0.8936
## IEAA 0.3075 0.8200
## EEAA 0.6635 0.8847
Linear relation
Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]
## coefficient std pval pval_BHadj
## AgeAccelerationResidual -1.03 0.74 0.17 0.17
## AgeAccelerationResidualHannum -0.70 0.64 0.28 0.37
## AgeAccelPheno -0.06 0.65 0.93 0.93
## DNAmAgeSkinBloodClockAdjAge -0.08 0.53 0.88 0.88
## AgeAccelGrim -0.11 0.47 0.81 0.82
## DNAmTLAdjAge -0.02 0.03 0.60 0.60
## IEAA -0.98 0.67 0.15 0.15
## EEAA -0.72 0.81 0.38 0.47
Cumulative lifetime (self-reported) fuel type
The numbers of observations with each cumulative lifetime fuel type:
##
## Mix Smoky
## 82 37
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon
\end{aligned}
\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.3222 0.5415
## Hannum EAA 0.4061 0.5415
## PhenoAge EAA 0.6397 0.7311
## Skin&Blood EAA 0.9331 0.9331
## GrimAge EAA 0.0245 0.1960
## DNAmTL 0.3396 0.5415
## IEAA 0.0940 0.3760
## EEAA 0.2773 0.5415
Linear regression
In the following section, we performed linear regression with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon
\end{aligned}
\]
where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.
The estimations of \(\beta_0\) and \(\beta_1\) with given \(Y\) are shown below. The \(\beta_1\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the mix fuel type, while holding other variables constant”.
Sensitivity analyses
Likelihood ratio (LR) test (single model)
Full model: \[Y = \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]
\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.3532 0.8414
## Hannum EAA 0.7909 0.8414
## PhenoAge EAA 0.8253 0.8414
## Skin&Blood EAA 0.8414 0.8414
## GrimAge EAA 0.1805 0.7220
## DNAmTL 0.6405 0.8414
## IEAA 0.0759 0.6072
## EEAA 0.6484 0.8414
Linear relation
Use a trend test to estimate a linear relation across use categories (1=mix, 2=Smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]
## coefficient std pval pval_BHadj
## AgeAccelerationResidual -0.88 0.95 0.36 0.36
## AgeAccelerationResidualHannum 0.21 0.80 0.79 0.79
## AgeAccelPheno 0.18 0.81 0.83 0.83
## DNAmAgeSkinBloodClockAdjAge 0.14 0.69 0.84 0.84
## AgeAccelGrim 0.75 0.57 0.19 0.19
## DNAmTLAdjAge -0.02 0.04 0.64 0.64
## IEAA -1.52 0.86 0.08 0.16
## EEAA 0.46 1.02 0.65 0.65
Childhood (self-reported) fuel type
The numbers of observations with each current fuel type:
##
## Mix Smokeles Smoky Wood
## 53 5 47 11
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon
\end{aligned}
\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0412 0.1099
## Hannum EAA 0.1426 0.1901
## PhenoAge EAA 0.2872 0.3282
## Skin&Blood EAA 0.1345 0.1901
## GrimAge EAA 0.0051 0.0408
## DNAmTL 0.4625 0.4625
## IEAA 0.0379 0.1099
## EEAA 0.1276 0.1901
Linear regression
In the following section, we performed linear regression with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon
\end{aligned}
\] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.
The estimations of \(\beta_0\), \(\beta_1\), \(\beta_2\), and \(\beta_3\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), and \(\beta_3\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.
Sensitivity analyses
Likelihood ratio (LR) test (single model)
Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.
Full model: \[Y = \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.2833 0.6101
## Hannum EAA 0.3813 0.6101
## PhenoAge EAA 0.8336 0.8336
## Skin&Blood EAA 0.7398 0.8336
## GrimAge EAA 0.0146 0.1168
## DNAmTL 0.5919 0.7892
## IEAA 0.1220 0.4880
## EEAA 0.3340 0.6101
Linear relation
Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3 = mix coal, 4=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]
## coefficient std pval pval_BHadj
## AgeAccelerationResidual -0.70 0.50 0.16 0.16
## AgeAccelerationResidualHannum -0.50 0.42 0.24 0.30
## AgeAccelPheno -0.13 0.43 0.77 0.93
## DNAmAgeSkinBloodClockAdjAge 0.02 0.36 0.95 0.95
## AgeAccelGrim 0.27 0.30 0.37 0.37
## DNAmTLAdjAge 0.01 0.02 0.62 0.99
## IEAA -0.87 0.44 0.05 0.06
## EEAA -0.52 0.53 0.33 0.39
Ambient Exposure
Linear regression (simple model)
In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.
The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.
## X: Ambient Exposure Measurements:
## bap pm25
## Horvath EAA -0.007 (0.0048) 0.0018 (0.0024)
## Hannum EAA -0.0023 (0.0042) 0.0001 (0.002 )
## PhenoAge EAA -0.002 (0.0045) -0.001 (0.0022)
## Skin&Blood EAA 0.0029 (0.0034) -0.0005 (0.0017)
## GrimAge EAA 0.0017 (0.0031) 0.0013 (0.0015)
## DNAmTL 0 (0.0002) -0.0001 (0.0001)
## IEAA -0.0098* (0.0042) 0.0015 (0.0022)
## EEAA -0.0012 (0.0054) 0.0003 (0.0026)
## [P<0.001: ***; P<0.01: **; P<0.05: *] <NA>
## ANY BPE BaA
## Horvath EAA 0.0009 ** (0.0003) -0.0072 (0.0046) -0.0042 (0.0029)
## Hannum EAA 0.0004 (0.0003) -0.0032 (0.004 ) -0.0015 (0.0026)
## PhenoAge EAA 0.0006 * (0.0003) -0.0037 (0.0043) -0.0004 (0.0027)
## Skin&Blood EAA 0.0004 (0.0002) 0.0026 (0.0032) 0.0013 (0.0021)
## GrimAge EAA 0.0007 ***(0.0002) 0.0013 (0.0029) 0.0014 (0.0019)
## DNAmTL 0 (0 ) 0 (0.0002) 0 (0.0001)
## IEAA 0.0006 * (0.0003) -0.0103* (0.004 ) -0.0055* (0.0026)
## EEAA 0.0006 (0.0003) -0.0023 (0.0051) -0.0007 (0.0032)
## <NA> <NA> <NA>
## BbF BkF CHR
## Horvath EAA -0.0034 (0.003 ) -0.0197 (0.0133) -0.0034 (0.0032)
## Hannum EAA -0.0017 (0.0026) -0.0082 (0.0117) -0.0017 (0.0028)
## PhenoAge EAA -0.0009 (0.0028) -0.0063 (0.0124) -0.0001 (0.003 )
## Skin&Blood EAA 0.0014 (0.0021) 0.0063 (0.0094) 0.0013 (0.0022)
## GrimAge EAA 0.0013 (0.0019) 0.0058 (0.0085) 0.0019 (0.002 )
## DNAmTL 0 (0.0001) 0.0001 (0.0005) 0 (0.0001)
## IEAA -0.0049 (0.0026) -0.0269* (0.0117) -0.0048 (0.0028)
## EEAA -0.0009 (0.0033) -0.0047 (0.0148) -0.0008 (0.0035)
## <NA> <NA> <NA>
## DBA FLT FLU
## Horvath EAA -0.0193 (0.0117) -0.0061* (0.0031) 0.0008 (0.0008)
## Hannum EAA -0.0103 (0.0103) -0.0025 (0.0027) -0.0003 (0.0007)
## PhenoAge EAA -0.0046 (0.011 ) -0.0008 (0.0029) 0.0007 (0.0007)
## Skin&Blood EAA 0.0043 (0.0083) -0.0002 (0.0022) 0.0005 (0.0005)
## GrimAge EAA 0.0044 (0.0075) 0.0012 (0.002 ) 0.0009 (0.0005)
## DNAmTL -0.0001 (0.0005) 0.0001 (0.0001) 0 (0 )
## IEAA -0.0267* (0.0102) -0.0064* (0.0027) 0.0006 (0.0007)
## EEAA -0.0075 (0.0131) -0.0024 (0.0034) -0.0001 (0.0009)
## <NA> <NA> <NA>
## IPY NAP PHE
## Horvath EAA -0.0112 (0.0082) 0.0002 ** (0.0001) 0.0006 (0.0005)
## Hannum EAA -0.0031 (0.0072) 0.0001 (0.0001) -0.0001 (0.0004)
## PhenoAge EAA -0.0054 (0.0077) 0.0001 * (0.0001) 0.0005 (0.0005)
## Skin&Blood EAA 0.0055 (0.0058) 0.0001 (0 ) 0.0003 (0.0004)
## GrimAge EAA 0.0041 (0.0053) 0.0001 ***(0 ) 0.0006 * (0.0003)
## DNAmTL 0.0001 (0.0003) 0 (0 ) 0 (0 )
## IEAA -0.0173* (0.0072) 0.0001 * (0.0001) 0.0005 (0.0004)
## EEAA -0.0021 (0.0092) 0.0001 (0.0001) 0 (0.0006)
## <NA> <NA> <NA>
## PYR
## Horvath EAA -0.0054 (0.003 )
## Hannum EAA -0.002 (0.0026)
## PhenoAge EAA -0.0006 (0.0028)
## Skin&Blood EAA 0.0002 (0.0021)
## GrimAge EAA 0.0012 (0.0019)
## DNAmTL 0.0001 (0.0001)
## IEAA -0.006 * (0.0026)
## EEAA -0.0018 (0.0034)
## <NA>
Urinary Measurements
Linear regression (simple model)
In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary measurements.
The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”. 
## X: Urinary Measurements:
## Benzanthracene_Chrysene Naphthalene
## Horvath EAA -0.0361 (0.1332) -0.0015* (0.0006)
## Hannum EAA 0.0486 (0.1146) -0.0012* (0.0005)
## PhenoAge EAA -0.0481 (0.122 ) -0.0011 (0.0006)
## Skin&Blood EAA 0.084 (0.0959) -0.0015***(0.0004)
## GrimAge EAA 0.1136 (0.0845) 0 (0.0004)
## DNAmTL -0.0043 (0.005 ) 0 (0 )
## IEAA -0.1071 (0.1221) -0.001 (0.0006)
## EEAA 0.0996 (0.1458) -0.0012 (0.0007)
## [P<0.001: ***; P<0.01: **; P<0.05: *] <NA>
## 2.Methylnaphthalene 1.Methylnaphthalene Acenaphthene
## Horvath EAA -0.0076 (0.0072) -0.0105 (0.0175) 0.0223 (0.0388)
## Hannum EAA -0.004 (0.0063) -0.0191 (0.0149) 0.0363 (0.0332)
## PhenoAge EAA -0.0096 (0.0066) -0.021 (0.0159) 0.0897 * (0.0348)
## Skin&Blood EAA -0.0074 (0.0053) -0.0251* (0.0125) -0.0142 (0.0282)
## GrimAge EAA 0.0073 (0.0047) 0.0115 (0.0111) 0.0636 ** (0.0241)
## DNAmTL -0.0003 (0.0003) -0.0006 (0.0007) -0.0012 (0.0015)
## IEAA -0.0049 (0.0067) -0.0034 (0.016 ) 0.0423 (0.0355)
## EEAA -0.0024 (0.008 ) -0.017 (0.0192) 0.0427 (0.0424)
## <NA> <NA> <NA>
## Phenanthrene_Anthracene Fluoranthene Pyrene
## Horvath EAA 0.0031 * (0.0015) 0.0312 (0.0176) 0.0731 (0.8107)
## Hannum EAA 0 (0.0013) 0.0012 (0.0153) 0.5231 (0.7025)
## PhenoAge EAA 0.0016 (0.0014) 0.0193 (0.0163) 1.1583 (0.7147)
## Skin&Blood EAA 0.0002 (0.0011) 0.0076 (0.0129) 0.4467 (0.5627)
## GrimAge EAA 0.0029 ** (0.0009) 0.0336 ** (0.011 ) 0.9093 (0.501 )
## DNAmTL -0.0002** (0.0001) -0.002 ** (0.0007) -0.0252 (0.0306)
## IEAA 0.0026 (0.0014) 0.0252 (0.0162) 0.3662 (0.7378)
## EEAA 0.0005 (0.0017) 0.0062 (0.0195) 0.7881 (0.8911)
## <NA> <NA> <NA>